• Korean Journal of Environment and Ecology
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• v.24 no.4
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• pp.395-405
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• 2010

This study was carried out to offer basic data to minimize the indiscreet development and damage of coastal wetlands through an evaluation from an ecological standpoint highlighting the importance of the coastal wetland in South Gyeongsang Province, Korea. The result of the macrobenthos survey for the coastal wetland assessment showed that Dongdal and Hwasan-ri, Yongnam-myeon, and Tongyeong city had the largest species number; Oegan-ri and Naegan-ri, Geoje-myeon, and Geoje city had the largest population and biomass; and Miryong-ri, Samsan-myeon, Goseong-gun had the highest species diversity. In the halophytes survey, Imyeong-ri, Jinjeon-myeon, Masan city and Oegan-ri and Naegan-ri, Geoje-myeon, Geoje city had the large character species and companion species. The evaluation results of the ecological values of the coastal wetlands were categorized into five grades based on the field surveys, and the sedimentary environment factor in the case of Danghang-ri, Hoehwa-myeon, and Goseong-gun; Miryong-ri, Samsan-myeon, Goseong-gun; Guho-ri, Gonyang-myeon, Sacheon city; Sulsang-ri Yangpo-ri, Jingyo-myeon, Hadong-gun; and Seokpyeong-ri, Idong-myeon, Namhae-gun, were appraised at the highest rating of grade II. The halophytes factor in the case of Imyeong-ri, Jinjeon-myeon, Masan city, Dongdal-ri and Hwasan-ri, Yongnam-myeon, Tongyeong city and Oegan-ri and Naegan-ri, Geoje-myeon, Geoje city, were highly evaluated as grade II. The macrobenthos factor in the case of Imyeong-ri, Jinjeon-myeon, Masan city and Oegan-ri and Naegan-ri, Geoje-myeon, Geoje city was highly evaluated as grade II. The final evaluation grade was calculated by the mean values of three evaluation factors, and Imyeong-ri, Jinjeon-myeon, Masan city and Oegan-ri and Naegan-ri, Geoje-myeon, and Geoje city had the highest rating of II. On the other hand, Seokpyeong-ri, Idong-myeon, Namhae-gun had the lowest rating of IV. These locations will require future research to survey and monitor the coastal wetland ecosystems by season, in addition to the construction of the GIS-based wetland information system with a view to manage the coastal wetlands.

• Korean Journal of Environment and Ecology
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• v.34 no.5
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• pp.446-453
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• 2020

This study built a model to predict the daily number of visitors to 18 trails in the Taebaeksan National Park using the auto-counter system data to analyze the factors affecting the daily number of visitors to each trail and classified the trails by visitors' behaviors. Results of the multiple regression models with the daily number of visitors of the 18 trails indicated that the events, such as the National Foundation Day celebration of Snow Festival, affected the number of visitors of all of the 18 trails and were the most critical factor that determined the daily number of visitors to the Taebaeksan National Park. The long-holidays of three days or longer and other national holidays also affected the daily number of visitors to the trails. Precipitation had a negative impact on the number of visitors of trails where the intention of most visitors was for sightseeing or camping instead of hiking, whereas had no significant impacts on the number of visitors of trails where many visitors intended for hiking. It indicated that visitors who intended for hiking went ahead hiking even if the weather was poor. The effects of temperature had a positive effect on the number of visitors who intended for hiking but a negative effect on the number of visitor to the trails near Danggol Plaza where the Snow Festival was held in each winter, suggesting that the impact of the Snow Festival was the deterministic factor for trail management. Results of K-mean clustering showed that the 18 trails of the Taekbaeksan National Park could be classified into three types: those affected by the Snow Festival (type 1), those that have sightseeing points and so were visited mostly by non-hikers (type 2), and those visited mostly by hikers (type 3). Since visitor behaviors and illegal actions differ according to the trail type, this study's results can be used to prepare a trail management plan based on the trail characteristics.

• Korean Journal of Plant Resources
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• v.29 no.4
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• pp.495-503
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• 2016

In the present study, we investigated the plant growth characteristics and biological activity of four asparagus cultivars grown using two cultural methods and tested the possibility of domestic open field. The number of shoots, buds, roots, shoot and root fresh and dry weights, and total dry weight of the four asparagus cultivars grown in a plastic house were higher than those of the same cultivars grown in an open field. Of the cultivars grown in the open field, Jersey Giant had greater shoot number than the other cultivars. In plastic house cultivation, the number of buds in Jersey Supreme was greater than the other cultivars. The total flavonoid content of the Jersey Giant was greater than the other cultivars, but cultural method was unaffected. The total polyphenol contents in asparagus cultivars grown in the plastic house were higher than those of cultivars grown in the open field. The total polyphenol content of the Jersey Giant grown the plastic house was significantly higher than those of other cultivars. Antioxidant activity such as catalase (CAT) and peroxidase (POX) did not differ significantly with cultural methods and among the cultivars. Ascorbate peroxidase (APX) activity of asparagus cultivars grown in the open field was higher than that of cultivars grown in the greenhouse; the highest APX activity was detected in UC157. Thus, greenhouse cultivation is expected to improve plant growth characteristics and biological activities of asparagus cultivars; each cultural method should be considered when selecting a suitable cultivar for high yield and high bioactive compound content.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Journal of the Microelectronics and Packaging Society
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• v.25 no.1
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• pp.1-10
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• 2018

Recently, there has been rapid development in the field of flexible electronic devices, such as organic light emitting diodes (OLEDs), organic solar cells and flexible sensors. Encapsulation process is added to protect the flexible electronic devices from exposure to oxygen and moisture in the air. Using numerical simulation, we investigated the effects of the encapsulation layer on mechanical stability of the silicon chip, especially the fracture performance of center crack in multi-layer package for various loading condition. The multi-layer package is categorized in two type - a wide chip model in which the chip has a large width and encapsulation layer covers only the chip, and a narrow chip model in which the chip covers both the substrate and the chip with smaller width than the substrate. In the wide chip model where the external load acts directly on the chip, the encapsulation layer with high stiffness enhanced the crack resistance of the film chip as the thickness of the encapsulation layer increased regardless of loading conditions. In contrast, the encapsulation layer with high stiffness reduced the crack resistance of the film chip in the narrow chip model for the case of external tensile strain loading. This is because the external load is transferred to the chip through the encapsulation layer and the small load acts on the chip for the weak encapsulation layer in the narrow chip model. When the bending moment acts on the narrow model, thin encapsulation layer and thick encapsulation layer show the opposite results since the neutral axis is moving toward the chip with a crack and load acting on chip decreases consequently as the thickness of encapsulation layer increases. The present study is expected to provide practical design guidance to enhance the durability and fracture performance of the silicon chip in the multilayer package with encapsulation layer.

• Journal of Science Education
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• v.39 no.3
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• pp.358-375
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• 2015

The purposes of this study were to investigate the value, necessity and legitimacy of STEAM Education and to propose practical approaching methods for STEAM Education to be applicable in Korea through a variety of literature review, case studies and collecting suggestions from domestic and international educational experts. The research questions are as follows: (1) To investigate the perception, understanding and recognitions of domestic and foreign professionals in STEAM education. (2) To analyze policy implications for an improvement in STEAM. The following aspects of STEAM were found to be challenges in our current STEAM policy after analyzing multiple questionnaires with the professionals and case studies including their experiences, understanding, supports and directions of the policy from the governments. The results indicate that (1) there was a lack of precise and conceptual understanding of STEAM in respect to experience. Training sessions for teachers in this field to help transform their perception is necessary. Development of practical programs with an easy access is also required. It is important to get the aims of related educational activities recognized by the professionals and established standards for an evaluation. The experts perceived that a theme-based learning is the most preferred and effective approaching method and the programs that develop creative thinking and learning applicable to practice are required to promote. (2) The results indicate that there was a lack of programs and inducements for supporting outstanding STEAM educators. It is shown that making an appropriate environment for STEAM education takes the first priority before training numbers of teachers unilaterally, thus securing enough budget seems critical. The professionals also emphasize on developing specialized teaching materials that include diverse inter-related subjects such as science technology, engineering, arts and humanities and social science with diverse viewpoints and advanced technology. This work requires a STEAM network for teachers to link up and share their materials, documents and experiences. It is necessary to get corporations, universities, and research centers participated in the network. (3) With respect to direction, it is necessary to propose policy that makes STEAM education ordinary and more practical in the present education system. The professionals have recommended training sessions that help develop creative thinking and amalgamative problem-solving techniques. They require reducing the workload of teachers and changing teachers' perspectives towards STEAM. They further urge a tight cooperation between departments of the government related with STEAM.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.25 no.3
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• pp.366-379
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• 2015

Objectives: The objective of this study is to assess airborne particulate matter(PM) pollution and its effect on health of residents living near Ansim Briquette Fuel Complex in Daegu metropolitan region. Methods: The California Puff(CALPUFF) dispersion model, version 5.8, which can estimate the dispersion direction and range of airborn $PM_{10}$ was used to determine the possible areas affected by $PM_{10}$ pollutants emitted from Ansim briquette fuel complex. The CALPUFF modeling with 200 m grid-cell resolution was performed based on $PM_{10}$ emissions estimated from the amount of coal consumption in the fuel complex for four months in 2012. The Weather Research and Forecasting(WRF) fields were processed using CALMET to produce CALPUFF-ready meteorological inputs. Also, the distance from Ansim Briquette Fuel Complex to the residence of each environmental pneumoconiosis patient was analyzed. In addition, the affecting region of the pollutants emitted from briquette factories in Ansim Briquette Fuel Complex was determined. Results: CALPUFF modeling results showed that the highest concentrations of $PM_{10}$ were found near around the fuel complex. The modeled $PM_{10}$ distributions were characterized by significant decreases in concentration with distance from the complex. Seasonally, the highest concentration of $45{\mu}g/m^3$ was calculated in October which was mostly due to the distinct variation of amount of emission. Additional modeling with the maximum $PM_{10}$ emission of about 88 tons per year in 1986 showed that the highest concentration in October was nearly increased by 8 times than the concentration modeled with emission of 2010. As a result of medical examination and interviews for the residents in Ansim Briquette Fuel Complex and its surroundings, 8 environmental pneumoconiosis patients were found. These patients do not have occupational exposure and history. These patients have lived 0.3~1.1 km area in Ansim Briquette Fuel Complex and its surroundings. Conclusions: Airborne particles emitted from Ansim Briquette Fuel Complex can contribute to significant increase in $PM_{10}$ concentration in residential areas near around the complex. Especially, the residents near fuel complex may exposed to the pollutants emitted from the factories in Ansim Briquette Fuel Complex.

• Applied Chemistry for Engineering
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• v.27 no.4
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• pp.391-396
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• 2016

The absorption efficiency of amine $CO_2$ absorbent (KoSol-5) developed by KEPCO research institute was evaluated using a 0.1 MW test bed. The performance of post-combustion technology to capture two tons of $CO_2$ per day from a slipstream of the flue gas from a 500 MW coal-fired power station was first confirmed in Korea. Also the analysis of the absorbent regeneration energy was conducted to suggest the reliable data for the KoSol-5 absorbent performance. And we tested energy reduction effects by improving the absorption tower inter-cooling system. Overall results showed that the $CO_2$ removal rate met the technical guideline ($CO_2$ removal rate : 90%) suggested by IEA-GHG. Also the regeneration energy of the KoSol-5 showed about $3.05GJ/tonCO_2$ which was about 25% reduction in the regeneration energy compared to that of using the commercial absorbent MEA (Monoethanolamine). Based on current experiments, the KoSol-5 absorbent showed high efficiency for $CO_2$ capture. It is expected that the application of KoSol-5 to commercial scale $CO_2$ capture plants could dramatically reduce $CO_2$ capture costs.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.9
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• pp.917-925
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• 2006

The aim of our research was to apply experimental design methodology in the optimization of photocatalytic degradation of azo dye(Reactive orange 16). The reactions were mathematically described as a function of parameters amount of $TiO_2(x_1)$, and dye concentration($x_2$) being modeled by the use of the Box-Behnken method. The results show that the responses of color removal(%)($Y_1$) in photocatalysis of dyes were significantly affected by the synergistic effect of linear term of $TiO_2(x_1)$ and dye concentration($x_2$). Significant factors and synergistic effects for the $COD_{Cr}$, removal(%)($Y_2$) were the linear term of $TiO_2(x_1)$ and dye concentration($x_2$). However, the quadratic term of $TiO_2(x_1^2)$ and dye concentration($x_2^2$) had an antagonistic effect on $Y_1$ and $Y_2$ responses. Canonical analysis indicates that the stationary point was a saddle point for $Y_1$ and $Y_2$, respectively. The estimated ridge of maximum responses and optimal conditions for $Y_1:(X_1,\;X_2)$=(1.11 g/L, 51.2 mg/L) and $Y_2:(X_1,\;X_2)$=(1.42 g/L, 72.83 mg/L) using canonical analysis was 93% and 73%, respectively.